Create a web page presentation using R Markdown that features a plot created with Plotly. Host your webpage on either GitHub Pages, RPubs, or NeoCities. Your webpage must contain the date that you created the document, and it must contain a plot created with Plotly.
Here I am, again, loading the “Cyclistic” dataset provided by Google. It is 12 separate CSVs, one for each month, containing geocoded data for bike-share usage in Chicago. For the Plotly visualizations, I will be using the ggplotly() function as it is easier to work with ggplot2.
librarian::shelf(dplyr, purrr, data.table, lubridate, ggplot2, plotly)##
## The 'cran_repo' argument in shelf() was not set, so it will use
## cran_repo = 'https://cran.r-project.org' by default.
##
## To avoid this message, set the 'cran_repo' argument to a CRAN
## mirror URL (see https://cran.r-project.org/mirrors.html) or set
## 'quiet = TRUE'.mydata <- do.call(rbind, lapply(list.files(pattern="*.csv"), fread))
mydata <- mydata %>%
select(-c(ride_id, start_station_name, end_station_name)) %>%
mutate(ride_length = as.numeric(ended_at-started_at),
ended_at = ymd_hms(ended_at),
started_at = ymd_hms(started_at),
day = factor(weekdays(started_at), c("Monday", "Tuesday", "Wednesday", "Thursday","Friday", "Saturday", "Sunday")),
member_casual = as.factor(stringr::str_to_title(member_casual)))
mydata$ride_length <- replace(mydata$ride_length, which(mydata$ride_length < 0), NA)
summary(mydata)## rideable_type started_at ended_at
## Length:4073561 Min. :2020-06-03 05:59:59 Min. :2020-06-03 06:03:37
## Class :character 1st Qu.:2020-08-07 19:09:29 1st Qu.:2020-08-07 19:39:10
## Mode :character Median :2020-09-30 07:36:28 Median :2020-09-30 07:51:42
## Mean :2020-11-08 09:07:38 Mean :2020-11-08 09:31:51
## 3rd Qu.:2021-03-13 10:03:09 3rd Qu.:2021-03-13 10:22:00
## Max. :2021-05-31 23:59:16 Max. :2021-06-10 22:17:11
##
## start_station_id end_station_id start_lat start_lng
## Length:4073561 Length:4073561 Min. :41.64 Min. :-87.87
## Class :character Class :character 1st Qu.:41.88 1st Qu.:-87.66
## Mode :character Mode :character Median :41.90 Median :-87.64
## Mean :41.90 Mean :-87.64
## 3rd Qu.:41.93 3rd Qu.:-87.63
## Max. :42.08 Max. :-87.52
##
## end_lat end_lng member_casual ride_length
## Min. :41.54 Min. :-88.07 Casual:1713356 Min. : 0
## 1st Qu.:41.88 1st Qu.:-87.66 Member:2360205 1st Qu.: 462
## Median :41.90 Median :-87.64 Median : 843
## Mean :41.90 Mean :-87.64 Mean : 1617
## 3rd Qu.:41.93 3rd Qu.:-87.63 3rd Qu.: 1554
## Max. :42.16 Max. :-87.44 Max. :3257001
## NA's :5037 NA's :5037 NA's :10336
## day
## Monday :504915
## Tuesday :505455
## Wednesday:531021
## Thursday :534848
## Friday :599049
## Saturday :758984
## Sunday :639289This graph shows a histogram of ride length in seconds with respect to the member category. As you can see, non-members (“casual”) tend to ride for a longer length of time.
ggplotly(
ggplot(data = mydata) +
geom_histogram(aes(x=ride_length, fill = member_casual))+
xlim(0,5000) +
labs(fill = "Casual User or Member?") +
xlab("Ride Length (s)") +
ggtitle(("Histogram of Ride Length by Member Status")) +
theme_bw()
)This graph shows the dsitribution of the average # of rides per day of the week with respect to the member category. As you can see, member bike usage is less dependent of the day, while non-member usage is focused on the weekends.
ggplotly(
ggplot(mydata) +
geom_bar(aes(x=day, fill = member_casual), position = 'dodge')+
labs(fill = "Casual User or Member?") +
xlab("Day of the Week") +
ggtitle(("Frequencies of Weekday Usage by Member Status")) +
theme_bw()
)